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Presenters: Antonio Regalado (Senior Editor for Biomed, MIT Tech Review), Othman Laraki (Co-Founder & CEO, Color Genomics), Katherine Chou (Head of Product, AI in Health, Google), Derek Zu (Strategy & Products, Baidu USA), Qirong Ho (Chief Technology Officer, Petuum), Nikjil Jain (Founder & CEO, ObEN) To read the full report click here for the digital edition. The American healthcare industry is paradoxical. Though approximately 20 percent of GDP is spent on healthcare, the quality of care has not improved despite spending increases and technological advancements. As healthcare spending doubles roughly every decade with individuals’ longevity remaining constant, Antonio Regalado, the panel’s moderator, asserted that the industry is experiencing Eroom’s law. Because of healthcare’s large impact both in economic and human wellness terms, artificial intelligence has the potential to overcome healthcare’s paradox to better the healthcare industry and mankind. The “AI in Healthcare” panel focused on ways to apply AI to improve the quality of healthcare while lowering its costs. Recognizing that healthcare has become more monopolistic, Othman Laraki keyed in on the structural challenges that the U.S. healthcare industry confronts. To address a growing need for effective and efficient care in the face of structural problems, the speakers underscored AI and its role in unlocking new medicinal innovations and treatment options. The panel assembled speakers involved with AI applications into healthcare. As engineers, the speakers took a tech approach to discussing the healthcare industry—each applying their expertise and company involvement to discuss how AI will supplement human intellect to overcome stagnation in quality of care.

“We have this paradox in healthcare that despite spending more, we’re not really getting that much bang for our buck.” -Antonio Regalado

  KEY TAKEAWAYS Healthcare is an entrenched industry, but AI can disrupt it. Healthcare is ripe for disruption. As an industry that has not experienced significant growth, healthcare can benefit from information technologies and AI to capitalize on government spending and technological advancements. By identifying causes of entrenchment and anticipating ways that AI will disrupt the current system, healthcare can benefit from technology and improve the industry as a whole. Healthcare is entrenched because of structural challenges. Healthcare’s value chain has monopolistic layers. The small number of insurance companies with high market caps, Laraki argues, restrict market forces from letting the best healthcare product win in terms of beating out competitors and lowering product costs within the confines of supply and demand. Other structural challenges relate to medical records and accompanying ownership issues. Many Americans, according to Nikjil Jain, do not have access to their own medical records and do not know who does have ownership. These ownership and monopoly challenges have restricted the healthcare industry from improving quality of care and lowering costs. AI can cause regulatory and payment disruption. When people think of AI disruption, they associate it with job displacement. Derek Zu, however, argues that applying more artificial intelligence into healthcare will disrupt the industry’s regulatory and pay structures by transitioning into a value-based structure, rather than causing unemployment. Value-based care is more feasible today due to machine learning’s ability to take in large amounts of surrounding data on an individual basis. This technology then predicts an individual’s type of health risk, intervenes early on and prevents some of those risks for better patient outcomes and lower overall costs. Artificial intelligence needs to be trusted. People must trust artificial intelligence and its capabilities in order for AI to positively impact the healthcare industry. If the technology is distrusted, it cannot become operational and its abilities will remain unutilized. In order to foster trust, technology needs to perform well and meet the desired outcomes. Humans must also change their mindsets to recognize that AI is a tool rather than a replacement for humans and can improve healthcare and the overall human experience. Katherine Chou predicts that, through human-machine complementarity, AI will become more trusted if applied to preventive and incremental medicine under a value-based system. Change will take time. The term “disruption” implies that artificial intelligence will sharply alter the status quo. Qirong Ho refutes that notion, instead predicting that AI will gradually change the current healthcare system. Hospital regulations and existing processes prevent overnight change from occurring and require incremental change to alter healthcare’s current proceedings.

“AI can augment what the clinicians can do in that space, since they actually no longer have to be necessarily focused just on the specifics of the images—now they can be thinking of the patient holistically.” - Katherine Chou

AI can improve clinical practices. Artificial intelligence increases efficiency and solves problems in a faster, more effective manner. This enables humans to leave data processing, detailed-oriented and habitual work to technology in order to specialize in more nuanced, interaction-based and holistic work that humans do best. Additionally, technological advancements can expand medical capabilities to sophisticate procedures, improving the lives of both physicians and patients. Artificial intelligence can streamline back office hospital processes. Hospitals are consumed by processes rather rather than the provision of care. Regalado notes that hospital staffs have grown, but the amount of doctor jobs have not—rather, jobs go to administrators, clerks and managers of hospital processes instead of towards people delivering the actual medical care. Ho linked this to hospitals’ regulatory report requirements and their backlogging since the current electronic databases are inadequate. Additional slow hospital procedures relate to electronic record systems and hospital interactions with insurance companies. Artificial intelligence can step in to structure unstructured data and interface with electronic record systems and insurance companies to streamline hospital processes. Not only will this improve the system, but it will provide staff with more patient interaction time. Technology can advance and improve medical procedures. Artificial intelligence is being used to make medical procedures less invasive and more effective. AI has helped realize diabetic retinopathy screening and liquid biopsy procedures, allowing doctors to do more groundbreaking surgeries while providing more options to patients. Zu and Chou highlight how AI can classify images, specifically using algorithms to identify disease areas and tumor regions in pathology slides. This gives doctors more information and greater proficiency to assess and treat patients. Artificial intelligence advantages and its use of genomic data, deep learning applications and next generation sequencing play a big role in preventative care and personalized medicine. AI use enables doctors to spend more time with patients. Artificial intelligence intends for doctors to spend less time with systems and more time with people by decreasing interface time with electronic record systems and insurance companies. Ultimately, artificial intelligence is about improving doctors’ work lives, whether by improving procedural capabilities, supplementing their own knowledge with AI capabilities or transitioning their time away from systems and towards patient interaction. Healthcare needs to become more available. In order for healthcare to become better, it has to become more available to individuals. As AI intends to simultaneously lower healthcare costs while improving its quality, patient wellness and health serve as primary metrics in determining progress. By using technologies to lengthen individuals’ life spans and make doctor care less expensive, healthcare will benefit more people. Availability implies more accessibility and affordability. Availability does not simply equate to accessibility. If healthcare is accessible but overly expensive, hospitals will deter individuals from receiving medical care. Thus, affordability is necessary as well. Recognizing a tradeoff between quality, cost and access within the healthcare system, Chou argues that AI can overcome this dilemma. Technologies should not only drive down costs, but also should additionally improve the quality of care and its availability since AI streamlines procedures and allows doctors to make an efficient use of their time. When medical staff is unavailable, AI should be used. Many locations in the developing world do not have access to the specialized medical staff found in the United States. However, in remote locations, artificial intelligence can serve as a potential substitute to certain types of doctors such as radiologists, pathologists, dermatologists and ophthalmologists since their line of work has an aspect of imaging classification and recognition—things that AI does well. Google, for instance, applied AI in India (where there is a lack of care access) when doing a diabetic retinopathy screening. AI’s ultimate goal is to make technology and care more available. Technology has a trickle-down effect. As groundbreaking technology is used initially in larger enterprises, overtime, its technology reaches households. The goal is to apply AI into healthcare so that its technologies pervade homes; providing individuals with availability to AI and more practical medical care. This is most important in cancer care as early prevention is key and cancer patients’ frequent hospitals to receive needed care. AI will change doctor roles in the future. The future of medicine and its interaction with artificial intelligence will alter traditional physician roles. As technology has improved, it can process abundant raw and unstructured data and use this information to detect risks and ailments early on, while moving forward to prevent such risks. That is not to say, however, that artificial intelligence can and will replace human doctors. Rather, technologies and humans will specialize in their separate comparative advantages to improve the healthcare system. AI should cover responsibilities that technology is better at than doctors. Physician responsibilities need to be redefined in the artificial intelligence era. This “peeling off” and “decomposition” of doctor tasks will allow technology to assume roles that are inherent to doctors and that technology can better perform. AI is best at imaging classifications related to medical imaging and processing granular and continuous data. Doctors, meanwhile, specialize in physician-patient interactions, interpreting models and applying processed data to analyze patients holistically. AI augments human intellect. AI can solve problems that the human mind cannot. However, it is not a competition between physicians and technology: both are needed in concert to lower healthcare costs and improve quality of care. Rather than being a rival, AI is an asset for humans to use in overcoming human shortcomings and pushing past barriers mainly related to processing continuous and unstructured data that previously obstructed human advancement. Human-technology interactions have different stages. Similar to how there are five levels to autonomous vehicles, Jain argues there are different levels of AI-care interaction. The care levels differ from AV levels since AI is less involved in the higher levels—the opposite of AV levels. The first level of care is a call center, in which artificial intelligence can completely commandeer humans in that role. As human interaction is not vital in that space, AI can help overcome bias, with Google’s “Duplex” serving as an example. The second stage relates to triage nurses that assign patients to the next level of care. Artificial intelligence can also play a direct role in this level, but with less interaction than the previous stage. Upon reaching the third level of senior nurses, care is more clinical and more analysis is required. At the third stage, there is a transition and less AI is applied. As healthcare continues to determine the level of AI involvement in hospitals, recognizing these levels of care will enable doctors and hospital staff to benefit most from AI involvement. Existing technologies should be repurposed into healthcare applications. AI is not confined to one industry or platform. As companies like Google have applied their technologies into healthcare, quality of care has improved without needing to reinvent the wheel and drive costs in developing new technology. Google has applied its capabilities to medical procedures. Specifically, when seeking to apply AI to improve fundus imaging, Google used the same convolutional neural network technology as a Google image search for ophthalmological procedures. Additionally, Google Translate technology and its accompanying sequencing models are used to predict a patient’s future health risks. By repurposing Google technology into healthcare, the company has shown that AI can effectively and practically be implemented into the industry. ObEN.AI repurposed its technologies to assist with mental illness. ObEN, a company that makes virtual copies of humans that “look like you, talk with you, act like you” transitioned into the healthcare industry by applying its “Avatar PAI” (Personal Artificial Intelligence) technology in providing care work. Recognizing that the older generation struggles to take their pills on time (or at all) without pressure from their grandchildren or primary doctors, the company set out to create avatars of busy primary care doctors and grandchildren to help overcome mental health challenges that seniors face. As there is a strong correlation to seniors not taking medication and increases in healthcare costs over time, the company’s application of AI focuses on lowering costs while improving care. AI needs guardrails to overcome liability issues. In the event that AI inaccurately diagnoses a patient, it is important that measures already exist that recognize the technology will not always be perfect. As similar liability dilemmas confront automated vehicles, we must consider how it will overcome such challenges, constructing guardrails to ensure that technology can endure setbacks. Liability guardrails begin at the product level. When transitioning to greater AI involvement in healthcare, doctors need to play a greater role in reviewing and signing off on AI results. Also, it is important that the technology has a built-in sorter that can identify false negatives so that doctors do not have to review all of the machine’s findings, defeating the point of using technology in the first place. Thus, guardrails should ensure that technology does not derail and patients are not worse off in the event that AI makes a mistake. Guardrails follow a concentric circle path from the model itself to society at large. The first guardrail, Chou notes, focuses on the model itself in ensuring that its technology is accurate and has been signed off by experts. The next level relates to the individual physician and making sure that they are qualified in interpreting the data provided. Moving outwards, the third circle corresponds to the clinical environment and whether the hospital has properly defined how it intends to use AI’s technology. The final guard rail adopts a historical and societal approach to assess the effect of AI application to society as a whole.  

But it’s difficult to think about value when we have no buoy for understanding it outside our traditional lenses: for example, our time, our job, and what others tell us they are worth in cash. This, largely, is the world’s paradigm for value so far. But understanding what value really means changes everything—and will be at the center of the decentralized revolution in global coordination that will unfold over the next decade. So, where do we begin?

Let’s start with gold.

Gold is an inherent value. When backing a market, gold allows us to grow a balanced economy well into the trillions. But why does it allow for massive stable markets to form around it? It is gold's permanence that creates stability. We understand that gold will always have value, because it is inherent in all of us, not just in one part of the world, but everywhere, not just today, but tomorrow and for the long haul.

In the 1930s when the gold standard was removed, we learned that the U.S. dollar didn’t need gold to back its economy to flourish. We learned that it was just a symbol for U.S. citizens to decentralize their coordination around the United States economy.

It turns out, common agreement is a philosophy for building shared economy.



And so it seems inherent value is a marker for us to begin exploring what the future could look like—a future beyond gold and the existing realm of credit. And so what else has inherent value? Is education as valuable as gold? What about healthcare? What about a vote that can’t be tampered with? What about an ID that can’t be stolen or erased? What about access to nutrition or clean water? You will find value everywhere you look.



It turns out, we’ve already done the legwork necessary to uncover the most elemental inherent values: The Sustainable Development Goals are commitments grown out of the drive to bring to life basic tenets of the Universal Declaration of Human Rights—the closest possible social contract we have to a global, common agreement.

We’ve already agreed, as a global community, to ensure inclusive and equitable access to quality education. We’ve already agreed to empower all women and girls, to ensure pure and clean water access for all, to promote health at all stages of life, and to end hunger.

We’ve already agreed.

Our agreements are grounded in deep value centers that are globally shared, but undervalued and unfulfilled. The reason for this is our inability to quantify intangible value. All of these rich, inherent values are still nebulous and fragmented in implementation—largely existing as ideals and blueprints for deep, globally shared common agreement. That is, we all agree education, health, and equality have value, but we lack common units for understanding who and who is not contributing value—leaving us to fumble in our own, uncoordinated siloes as we chase the phantoms of impact. In essence, we lack common currencies for our common agreements.

Now we find ourselves at the nexus of the real paradigm of Blockchain, allowing us to fuse economics with inherent value by proving the participation of some great human effort, then quantifying the impact of that effort in unforgeable and decentralized ledgers. It allows us to build economic models for tomorrow, that create wholly new markets and economies for and around each of the richest of human endeavors.



In late 2017 at the height of the Bitcoin bubble, without individual coordination, planning, or the help of institutions, almost $1 trillion was infused into blockchain markets. This is remarkable, and the revolution has only just begun. When you realize that Blockchain is in a similar stage of development as the internet pre-AOL, you will see a glimpse of the global transformation to come.



Only twice in the information age have we had such a paradigm shift in global infrastructure reform—the computer and the internet. While the computer taught us how to store and process data, the Internet built off that ability and furthered the conversation by teaching us how to transfer that information. Blockchain takes another massive step forward—it builds off the internet, adding to the story of information storage and transfer—but, it teaches us a new, priceless and not yet understood skill: how to transfer value.



This third wave kicked off with a rough start—as happens with the birth of new technologies and their corresponding liberties. Blockchain has, thus far, been totally unregulated. Many, doubtless, have taken advantage. A young child, stretching their arms for the first couple times might knock over a cookie jar or two. Eventually, however, they learn to use their faculties—for evil or for good. As such, while it’s wise to be skeptical at this phase in blockchain’s evolution, it’s important not to be blind to its remarkable implications in a post-regulated world, so that we may wield its faculties like a surgeon’s scalpel—not for evil or snake-oil sales, but for the creation of more good, for the flourishing of commonwealth.

But what of the volatility in blockchain markets? People agree Bitcoin has value, but they don’t understand why they are in agreement, and so cryptomarkets fluctuate violently.  Stable blockchain economies will require new symbolic gold standards that clearly articulate why someone would agree to support each market, to anchor common agreement with stability. The more globally shared these new value standards, the better.

Is education more valuable than gold? What about healthcare or nutrition or clean water?


We set out in 2018 to prove a hypothesis—we believe that if you back a cryptocurrency economy with a globally agreed upon inherent value like education, you can solve for volatility and stabilize a mature long lasting cryptomarket that awards everyone who adds value to that market in a decentralized way without the friction of individual partnerships.

What if education was a new gold standard?

And what if this new Learning Economy had protocols to award everyone who is helping to steward the growth of global education?



Education is a mountain. Everyone takes a different path to the top. Blockchain allows us to measure all of those unique learning pathways, online and in classrooms, into immutable blockchain Learning Ledgers.

By quantifying the true value of education, a whole economy can be built around it to pay students to learn, educators to create substantive courses, and stewards to help the Learning Economy grow. It was designed to provide a decentralized way for everyone adding value to global education to coordinate around the commonwealth without the friction of individual partnerships. Imagine the same for healthcare, nutrition, and our environment?



Imagine a world where we can pay refugees to learn languages as they find themselves in foreign lands, a world where we can pay those laid off by the tide of automation to retrain themselves for the new economy, a world where we can pay the next generation to prepare themselves for the unsolved problems of tomorrow.



Imagine new commonwealth economies that alleviate the global burdens of poverty, disease, hunger, inequality, ignorance, toxic water, and joblessness. Commonwealths that orbit inherent values, upheld by immutable blockchain protocols that reward anyone in the ecosystem stewarding the economy—whether that means feeding the hungry, providing aid for the global poor, delivering mosquito nets in malaria-ridden areas, or developing transformative technologies that can provide a Harvard-class education to anyone in the world willing to learn.


These worlds are not out of reach—we are only now opening our eyes to the horizons of blockchain, decentralized coordination, and new gold standards. Even though coordination is the last of the seventeen sustainable development goals, when solved, its tide will lift for the rest—a much-needed rocket fuel for global prosperity.

“Let us raise a standard to which the wise and the honest can repair.”  —George Washington
The views presented in this article are the author’s own and do not necessarily represent the views of any other organization.